Data-driven distributionally robust joint planning of distributed energy resources in active distribution network

With the increasing penetration of distributed energy resources (DERs) in the active distribution network (ADN), how to enable joint planning of DERs under the uncertainty of distributed generations (DGs) has become a challenging problem. This study establishes a two-stage joint planning model consi...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:IET generation, transmission & distribution Jg. 14; H. 9; S. 1653 - 1662
Hauptverfasser: Gao, Hongjun, Wang, Renjun, Liu, Youbo, Wang, Lingfeng, Xiang, Yingmeng, Liu, Junyong
Format: Journal Article
Sprache:Englisch
Veröffentlicht: The Institution of Engineering and Technology 11.05.2020
Schlagworte:
active distribution network
ADN
asynchronous generators
constraint generation algorithm
data‐driven distributionally robust joint planning
DERs
distributed energy resources
distributed energy storage systems
distributed generations
distributed power generation
distribution networks
energy storage
induction generator
integer programming
master problem
mixed‐integer nonlinear planning problem
nonlinear programming
optimal planning decision
power distribution planning
probability
Research Article
second‐order cone programming problem
sub‐problem framework
two‐stage data‐driven distributionally robust planning model
two‐stage joint planning model
worst probability distribution
nonlinear programming
distribution networks
integer programming
power distribution planning
distributed generations
master problem
distributed power generation
ADN
energy storage
distributed energy storage systems
worst probability distribution
two-stage joint planning model
active distribution network
sub-problem framework
probability
distributed energy resources
asynchronous generators
constraint generation algorithm
two-stage data-driven distributionally robust planning model
second-order cone programming problem
optimal planning decision
induction generator
DERs
data-driven distributionally robust joint planning
mixed-integer nonlinear planning problem
ISSN:1751-8687, 1751-8695
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:With the increasing penetration of distributed energy resources (DERs) in the active distribution network (ADN), how to enable joint planning of DERs under the uncertainty of distributed generations (DGs) has become a challenging problem. This study establishes a two-stage joint planning model considering doubly-fed induction generator, photovoltaics (PVs) with the ancillary services of PV inverter, distributed energy storage systems and different types of controllable loads in the ADN. To address the uncertainties of DGs, a two-stage data-driven distributionally robust planning model is constructed. The proposed model is solved in a ‘master and sub-problem’ framework by column-and-constraint generation algorithm, where the master problem is to minimise the total cost and find the optimal planning decision under the worst probability distributions, and the sub-problem is to find the worst probability distribution of given uncertain scenarios. Besides, the original mixed-integer non-linear planning problem is converted into a mixed-integer second-order cone programming problem through second-order cone relaxation, Big-M and piecewise linearisation method. The numerical results based on 33-bus system verify the effectiveness of the proposed model.
ISSN:1751-8687
1751-8695
DOI:10.1049/iet-gtd.2019.1565